






"""simple docstring"""







import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

lowerCAmelCase_						:     Any							  =   '''Usage of script: script_name <size_of_canvas:int>'''

lowerCAmelCase_						:     Optional[int]							  =   [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      [[False for i in range(a_		)] for j in range(a_		)]
       return canvas






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       for i, row in enumerate(a_		):
              for j, _ in enumerate(a_		):
                     UpperCAmelCase						     =      bool(random.getrandbits(1		)		)






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      np.array(a_		)
       UpperCAmelCase						     =      np.array(create_canvas(current_canvas.shape[0]		)		)
       for r, row in enumerate(a_		):
              for c, pt in enumerate(a_		):
                     UpperCAmelCase						     =      __judge_point(
                         a_						,   current_canvas[r - 1 : r + 2, c - 1 : c + 2]		)

       UpperCAmelCase						     =      next_gen_canvas
       del next_gen_canvas  # cleaning memory as we move on.
       UpperCAmelCase						     =      current_canvas.tolist()
       return return_canvas






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      0
       UpperCAmelCase						     =      0
       # finding dead or alive neighbours count.
       for i in neighbours:
              for status in i:
                     if status:
                            alive += 1
                     else:
                            dead += 1

              # handling duplicate entry for focus pt.
       if pt:
              alive -= 1
       else:
              dead -= 1

       # running the rules of game here.
       UpperCAmelCase						     =      pt
       if pt:
              if alive < 2:
                     UpperCAmelCase						     =      False
              elif alive == 2 or alive == 3:
                     UpperCAmelCase						     =      True
              elif alive > 3:
                     UpperCAmelCase						     =      False
       else:
              if alive == 3:
                     UpperCAmelCase						     =      True

       return state


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise Exception(usage_doc)

 lowerCAmelCase_						:     Dict							  =   int(sys.argv[1])
 # main working structure of this module.
 lowerCAmelCase_						:     Tuple							  =   create_canvas(canvas_size)
 seed(c)
 lowerCAmelCase_  ,       lowerCAmelCase_						:     Dict							  =   plt.subplots()
 fig.show()
 lowerCAmelCase_						:     List[Any]							  =   ListedColormap(['''w''', '''k'''])
 try:
  while True:
   lowerCAmelCase_						:     List[Any]							  =   run(c)
   ax.matshow(c, cmap=cmap)
   fig.canvas.draw()
   ax.cla()
 except KeyboardInterrupt:
  # do nothing.
  pass






'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """imagegpt"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """hidden_size""": """n_embd""",
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(						self   ,				UpperCamelCase=512 + 1   ,				UpperCamelCase=32 * 32   ,				UpperCamelCase=512   ,				UpperCamelCase=24   ,				UpperCamelCase=8   ,				UpperCamelCase=None   ,				UpperCamelCase="quick_gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=0.02   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     int:
     __a								=       vocab_size
     __a								=       n_positions
     __a								=       n_embd
     __a								=       n_layer
     __a								=       n_head
     __a								=       n_inner
     __a								=       activation_function
     __a								=       resid_pdrop
     __a								=       embd_pdrop
     __a								=       attn_pdrop
     __a								=       layer_norm_epsilon
     __a								=       initializer_range
     __a								=       scale_attn_weights
     __a								=       use_cache
     __a								=       scale_attn_by_inverse_layer_idx
     __a								=       reorder_and_upcast_attn
     __a								=       tie_word_embeddings

     super().__init__(tie_word_embeddings=UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('input_ids', {0: 'batch', 1: 'sequence'}),
         ]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = 1   ,				UpperCamelCase = -1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = 3   ,				UpperCamelCase = 32   ,				UpperCamelCase = 32   ,				)					->     Mapping[str, Any]:
     __a								=       self._generate_dummy_images(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
     __a								=       dict(preprocessor(images=UpperCamelCase   ,				return_tensors=UpperCamelCase   )   )

     return inputs



